GENDER BIAS IN ARTIFICIAL INTELLIGENCE : IMPLICATIONS FOR SOCIAL INEQUALITY

Abstract : Artificial Intelligence (AI) has become a pervasive component of modern society, influencing decision-making processes across sectors such as employment, healthcare, finance, and communication. Despite its promise of efficiency and objectivity, AI systems often reproduce and amplify existing social inequalities, particularly gender bias. This study examines how AI systems function as socio-technical artefacts that embed and perpetuate gender inequality through biased data, algorithmic design, and historical human prejudices. Drawing on feminist theory, intersectionality, African feminism, and womanism, the study situates AI bias within broader structures of patriarchy and social inequality. Using a qualitative research design grounded in interpretivism, the study employs content analysis and critical discourse analysis (CDA) to examine selected texts from media reports, academic publications, and institutional sources. The findings reveal that gender bias in AI manifests in multiple forms, including representational, structural, statistical, and algorithmic bias. These biases are evident in real-world applications such as recruitment systems, voice assistants, search engines, and predictive algorithms, where women are systematically underrepresented or disadvantaged. The analysis further shows that AI systems are not neutral technologies, but reflections of societal power relations shaped by male-dominated development teams and biased historical datasets. Consequently, AI reinforces stereotypes, limits women’s access to opportunities, and contributes to automated forms of inequality. The study concludes that addressing gender bias in AI requires not only technical interventions such as algorithmic fairness but also structural reforms that promote diversity, accountability, and ethical awareness in AI development and deployment. Keywords: gender bias, algorithmic discrimination, artificial intelligence, social inequality, feminist theory, intersectionality, African feminism, socio-technical systems.